17 lines
1.1 KiB
Markdown
17 lines
1.1 KiB
Markdown
# Starting project
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## Step 1. Create Map
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First of all we have to create map for TF Dataset. Using DataSetLoader we have to provide path to images folder and call get_classified_csv(). This will create a Map (Image Path with label).
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This will be later used for creating TF dataset.
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## Step 2. Create Dataset
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Then you can uncomment lines in main.py for generating dataset. Make sure that you have changed a path. Also change an image size, for this we have parameters like x and y. This way resized images will be saved to dataset. We recommend using x=35 and y=35. That's how you get the best results.
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## Step 3. Load datasets to kaggle
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Just create a new Dataset and move dataset_YxY after that add to notebook as input
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## Step 4. Evaluate in Kaggle
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Copy everything from Evaluator_Kaggle.ipynb in Notebook. Don't forget to turn on an GPU Accelarator. That way you will get result a lot faster.
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## Optional. Testing locally
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You can also train model and start tests locally. For that just start a main.py. Offcourse, don't forget to comment lines for creating a CSV and mapping. Also change a path to wished dataset. |